Multi-points assisted surrogate-based design and optimization of forklift key components

Multi-points assisted surrogate-based design and optimization of forklift key components | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-points assisted surrogate-based design and optimization of forklift key components wei zhang, Yujun Lu, Liye Lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3834486/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To better balance adaptive sampling strategies between global exploration and local exploitation and improve the prediction accuracy of the ensemble surrogate model. Based on the individual adaptive sampling (IAS) strategy, an efficient and robust PAI strategy for ensemble surrogate (ES) model (ES-PAI) is proposed. The surrogate model library built into the ES model consists of several typical individual surrogate (IS) models, such as PRS, RBF, Kriging, SVR. Based on the IAS strategy, each IS model in the surrogate model library is used to guide the selection of new infilling sample points, after which new sample points satisfying the diversity are selected by screening with the Euclidean distance criterion. Then, the new samples are added to the training point set to guide the construction of new surrogate models. The effect of IAS strategies used in the ES-PAI strategy and whether the strategy can be applied to most of currently available ensemble surrogate models are explored. Results show that the ES-PAI strategy always outperforms the IAS strategies in terms of global and local performance and is more robust. The ES-PAI strategy is applied into the design and optimization of the forklift gantry. In this practical problem, to save costs, deformation, stress, and mass of a forklift gantry are approximated by surrogate models. The weight of the forklift gantry was reduced by about 4.1% under the condition of stress and deformation. Parallel adaptive infilling strategy Expected improvement Optimization of forklift Surrogate model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3834486","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265798525,"identity":"3151907b-4d42-4125-bf66-6ad522305653","order_by":0,"name":"wei zhang","email":"","orcid":"","institution":"Zhejiang Sci- Tech University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"wei","middleName":"","lastName":"zhang","suffix":""},{"id":265798526,"identity":"005ddb28-b360-4308-9486-27f05a3c3869","order_by":1,"name":"Yujun Lu","email":"","orcid":"","institution":"Zhejiang Sci- Tech University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yujun","middleName":"","lastName":"Lu","suffix":""},{"id":265798527,"identity":"d8d3a50a-a380-44f5-b753-c7f33067002e","order_by":2,"name":"Liye Lv","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACPgbmhgMMDDZQLhsRWtgYGEFa0qCqidUCpA6TokUisfEwT8V5e/n5PQYMH8oOM/DPbiCopeEwz5nbzAbHeAwYZ5w7zCBx5wABLTwHGw7ztt1mM2DjMWDmbTvMYCCRQIyWf+d45NuAWv4SpYW9Eail4YAEA9BhzIzEajk451iygcGxtIKDPefSeSRuENDCz8x8+MObGjt7+ebDGx/8KLOW459BQAsIMPFAGQeAmAePQgRg/EGUslEwCkbBKBixAADEUD32is0OCwAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Sci- Tech University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Liye","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2024-01-04 11:59:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3834486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3834486/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56416016,"identity":"535b374c-764a-4a97-bac4-068a8ba72408","added_by":"auto","created_at":"2024-05-14 00:16:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1014398,"visible":true,"origin":"","legend":"","description":"","filename":"Multipointsassistedsurrogatebaseddesignandoptimizationofforkliftkeycomponentssmo.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3834486/v1_covered_06ce7366-0d67-4f66-9102-023265607fa0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-points assisted surrogate-based design and optimization of forklift key components","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parallel adaptive infilling strategy, Expected improvement, Optimization of forklift, Surrogate model","lastPublishedDoi":"10.21203/rs.3.rs-3834486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3834486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo better balance adaptive sampling strategies between global exploration and local exploitation and improve the prediction accuracy of the ensemble surrogate model. Based on the individual adaptive sampling (IAS) strategy, an efficient and robust PAI strategy for ensemble surrogate (ES) model (ES-PAI) is proposed. The surrogate model library built into the ES model consists of several typical individual surrogate (IS) models, such as PRS, RBF, Kriging, SVR. Based on the IAS strategy, each IS model in the surrogate model library is used to guide the selection of new infilling sample points, after which new sample points satisfying the diversity are selected by screening with the Euclidean distance criterion. Then, the new samples are added to the training point set to guide the construction of new surrogate models. The effect of IAS strategies used in the ES-PAI strategy and whether the strategy can be applied to most of currently available ensemble surrogate models are explored. Results show that the ES-PAI strategy always outperforms the IAS strategies in terms of global and local performance and is more robust. 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